Performance of Loan Repayment Determinants in Ethiopian Micro … · 2014-07-07 · reference of Addis Credit and Saving Institution, Addis Ababa, Ethiopia (Fikirte, 2011). She applied,

Eurasian Journal of Business and Economics 2014, 7 (13), 29-49

Performance of Loan Repayment

Determinants in Ethiopian Micro Finance - An

Analysis

Shaik Abdul Majeeb PASHA *

, Tolosa NEGESE **

Abstract

Micro finance involves the provision of micro-credit, savings, and other services to

the poor that are excluded by the commercial banks for collateral and other

reasons. Microfinance is relatively new to Ethiopia and came to existence during

1994-95. Out of which Sidama Micro Finance Institution (SMFI) is one among

31Micro Finance Institutions (MFIs) to serve needy people in Ethiopia. Based on this

researchers’ intended to study major socio- economic factors and loan related

factors that determines loan repayment performance of borrowers in SMFI. In fact,

the identifying and analyzing such determining factors of loan repayment rate is

vital in the achievement of profitability and sustainability of MFIs. In this

connection, researchers’ collected data from primary and secondary resources and

analyzed by using Binary logistic model is used. Through the study 14 determinants’

are selected for evaluation, out of which 9 variables are significant and remaining

insignificant are found. Based on the analysis, researchers are recommended that

proper training should be provided, reasonable amount of loan which should be

useful to their business. Further, more age people and well business experience

people can able to repay their loan amount` timely to micro finance institution.

Keywords: Micro Finance, Loan Repayment, Performance of Borrowers, Institution.

JEL Code Classification: G21, G20, G30, G02

*

Professor of Finance & Accounting, Arba Minch University, Ethiopia. E-mail: [email protected] **

Head of Dept. of Accounting and Finance, Arba Minch University, Ethiopia. E-mail: [email protected]

Shaik Abdul Majeeb PASHA & Tolosa NEGESE

Page | 30 EJBE 2014, 7 (13)

1. Introduction

In developing countries, including Ethiopia, micro financing institutions (MFIs)

emerged with unique opportunity to serve poor people who do not have access to

commercial banks. Microfinance involves the provision of micro-credit, savings, and

other services to the poor that are excluded by the commercial banks for collateral

and other reasons. Microfinance is relatively new to Ethiopia and came to existence

during 1994-95 with the government’s licensing and supervision of microfinance

institution proclamation (Zerai and Rani, 2012). The main objective of the these

institutions are they deliver micro-loans, micro-savings, micro-insurance, money

transfer, leasing, etc to a large number of productive resource-poor people in the

country in a cost-effective and sustainable way.

The objective of microfinance institutions as development organizations are to

service the financial needs of un-served or underserved markets as a means of

meeting development objectives such as to create employment, reduce poverty,

help to develop existing business or diversify their activities, empower women or

other disadvantaged population groups, and encourage the development of new

business ( Bayeh, 2012).

A large number of MFIs in Ethiopia has progressed significantly in terms of

sustainability. Although the development of MFIs started very recently, however,

the industry showed a remarkable growth. As of 2007, there are 27 MFIs registered

under National Bank of Ethiopia(NBE) had an active loan portfolio of about birr 2.7

billion delivered to 1.7 million active clients. At the same year they also mobilized

about 951 million birr of savings. Moreover about 38% of the clients of the MFIs are

females (Amaha, 2008). While the end of 2011, the total number of MFIs has risen

to 31 with 433 branches and 598 sub branches. At the same year the study shows

10 – 25% of the total micro finance demand in the country. The institutions have

extended total credit of 6.9 billion ETB to 2,470,611 active borrowers ( Mohana and

Ludego, 2013).

1.1. Statement of Problem

One way to tackle the loan repayment problem is to investigate the factors which

affect the loan repayment of MFIs., (Onyeagocha et al,2012) although loan

repayment is determined by willingness, ability and other characteristics of the

borrowers; businesses characteristics and characteristics of the lending institutions

including product designs and suitability of their products to borrowers. Regarding

the characteristics of borrowers, repayment of loans depends on the willingness

and ability of the borrowers to repay. Therefore, individual borrowers can either

repay their loans or choose to default. It is also true that the factors influencing

loan repayment capacity among borrowers are not only likely to differ by programs

but also differ from country to country depending on the domestic business and

economic environment (Tundui and Tundui, 2013).

Performance of Loan Repayment Determinants in Ethiopian Micro Finance - An Analysis

EJBE 2014, 7 (13) Page | 31

Sidama Micro financing institution (SMFI) is one of major micro financing

institutions of Ethiopia working to provide credit and saving services to urban and

rural poor who do not have access to financial services from formal banks in

Sidama Zone. However, to outreach large number of poor and lift themselves out

from poverty, the numbers of defaulters have been challenging the institution’s

social as well as financial objectives by retaining large amount of loan, as a result

this hinder it not to combat to ward poverty reduction strategy and its realization

of sustainability by diminishing loan repayment rate. Therefore, to alleviate

aforementioned problems the researcher intended to study major socio- economic

factors and loan related factors that determines loan repayment performance of

borrowers in SMFI.

1.2. Objective of the Study

The main objective of this study is to analyze and identify the determinants of loan

repayment performance of the borrowers in SMFI and specific objectives are:

• To analyze and identify the major socio-economic factors that influence loan

repayment rate of the borrowers.

• To investigate and differentiate loan and business related factors that affect

loan repayment performance of borrowers in SMFI

• To assess the problems and challenges that affects both institution and

borrowers regarding loan repayment process.

1.3. Significance and Limitations of the Study

The primary objective of micro financing institutions is providing financial services

to improve the living standards of the poor. For these the institutions to be able to

render such a services on the permanent basis, it has to realize its profitability and

sustainability. However, the profitability and sustainability of MFIs depends on the

borrowers’ action toward the loan repayment rate. The borrower’s action toward

loan repayment performance is determined by various factors.

During the study researchers find some limitations. These includes: shortage of

money, lack of enough time, lack of transportation access, and absence of

respondents at planned time during data collection. The data were collected from

respondents and study was conducted only in three branches out twenty one

branches of SMFI.

2. Micro Finance in Ethiopia

2.1. Overview of Micro Financing in Ethiopia

Many of the MFIs in Ethiopia provide similar financial products and use

predominantly the group lending methodology, while individual lending is

employed to a limited extent (Amaha, 2008). Group lending schemes induce

borrowers to engage in assertive matching wherein local knowledge about each


Page | 32 EJBE 2014, 7 (13)

other’s assets, capabilities, character traits are used to sort and self select (Rejaul

and Karim, 2008).

2.2. Sidama Micro Financing Institution (SMFI) Sharing Company

Though SMFI, evolved from the Sidama Rural Women’s Credit and Savings scheme

that was started in December 1994 and finally got operational license from

National Bank of Ethiopia in 1998 in accordance proclamation No. 40/1996.. It is

operating only in Sidama Zone; the Head quarter is in Hawassa, capital of SNNPRG

in Sidama zone.

The total number of employees in the institution is 270, out of which 67 are loan

officers; excluding 68 supportive staffs others are different level experts and

managers. In June 30, 2013, there were 16,716 loan clients; on average one loan

officer supervises around 250 loan clients.

Table 1: Performance of SMFI during June 2009-2013 (SMFI, 2013).

Year June 2009 June 2010 June 2011 June 2012 June 2013

No. of clients 3611 5335 10717 11485 16716

Average loan size 4747 4464 3974 5486 6157

Repayment rate% 82% 92% 94% 98% 96.94%

Default rate % 18% 8% 6% 2% 3.06%

Source: Sidama micro financing institution S.C (SMF, 2013)

2.3. Empirical studies on loan repayment performance

Several studies have been conducted in different developing countries regarding

determinants that affect loan repayment performance. The following are

determinants of loan repayment performances.

2.3.1. Empirical studies of other countries

The determinants of loan repayment under the indigenous financial system in

southeast, Nigeria (Eze and Ibekwe, 2007). They employed descriptive statistics and

multiple regressions to analyze the data. The analyzed data reveals that amount of

loan received, age of beneficiaries, house hold size, educational attainment, and

occupation can influence loan repayment.

Determinants of loan repayment performance of fishermen, Ghana employed

multiple regression analysis in their study. Their results revealed that low level of

education, lack of alternative income generating activity, cumbersome loan

processing procedures, they are likely to have high loan default. The study

identified fishing income, amount borrowed and size of loan invested into fishing as

significant predictors of loan repayment (Acquah and Addo, 2011).

The factors affecting loan repayment performance among Yam farmers in the Sene

District, Ghana are analyzed. Based on analysis, the factors that affect loan

repayment, he employed the Tobit model. According to the finding education,


EJBE 2014, 7 (13) Page | 33

experience, profit, age, supervision and off-farm income have positive effects on

loan repayment performance (Wongnaa and Awunyo,2013).

The causes of loan default within micro finance institutions in Kenya studied on

Causes of loan default within micro financing institutions in Kenya. The study found

out that loan repayment default was as result of non supervision of borrowers by

the MFIs (Okibo,2013).

2.3.2. Empirical Studies in Ethiopia

The loan repayment and its determinants in small scale enterprises financing are

investigated and evaluated, in case of private borrowers around Ziway area in

Ethiopia. The estimation result employed Tobit model. His result reveals that

having other source of income, education level, work experience in related

economic activity before the loan and engaging on economic activities other than

agriculture are enhancing loan repayment (Abrham,2002).

Factors that influence micro finance and loan repayment performance were

studied with particular reference to the Oromia Credit and Savings Share Company

(OCSSCO) in Kuyu, through the application of descriptive statistics and the probity

model, shows that education, income, loan supervision, suitability of repayment

period, and availability of other credit sources are important and significant factors

that enhance the loan repayment performance (Abafita,2003).

The determinants of loan repayment performance were studied with the specific

reference of Addis Credit and Saving Institution, Addis Ababa, Ethiopia (Fikirte,

2011). She applied, on repayment performance of borrowers, the weighted logit

model. Her result reveals that age was found to be statistically significant i.e as age

increased; the probability of being defaulter is decreased.

3. Methodology of the study

3.1. The description of study area

Out of 31 Micro Finance institutions of Ethiopia, researcher selected SMFI for study

of the above title. Sidama Administration Zone is found in SNNPR regional state and

it is one of the 13 zones in the region. This survey mainly considers three worda’s

branches of Arbegona, Bona, and Bensa in the Sidama Zone. Averagely

woredas’(District) covered an area of 372.81 km2 and total population of

507,825(Male 255704, and Female 252121).

3.1. 1.Research Design

The study employed explanatory research design with quantitative and qualitative

methods. The quantitative aspect of the data focused on description of socio-

economic variables, loan and related variables, and business related variables and

analysis of relationship among the dependent and explanatory variables of SMFI for

the study.


Page | 34 EJBE 2014, 7 (13)

3.2.2. Data Sources

The study employed both primary and secondary sources. Primary data sources are

the sample loan borrowers of both defaulters and non defaulters from each

branch. In support of primary sources, secondary data sources were obtained from

both head office, and branches’ managers concerned other officers and

unpublished works also.

3.2.3. Sampling Techniques

For this study multi-stage probability sampling techniques were used. At the first

stage, the Sidama micro finance institution was selected purposively due to so far

there is no scientific studies has been taken regarding to loan repayment

performance in this study area. So, researcher motivated to identify and analyze

the determinants of loan repayment performances of borrowers in SMFI. At the

second stage, all 21 branches were stratified in to three groups (A,B, C) based on

their organizational grades. Out of these groups, group “C” was randomly selected

for the study purpose. Out of it three branches of microfinance were selected

based on their long term of service provision.

At the fourth stage, the total borrowers’ populations of branches were stratified as

defaulters and non-defaulters. From total population of 2960 borrowers, 296 (10%)

sample respondents were selected through using simple random sampling

technique. The sampling respondents were taken from all active clients that have

been participated in the program during 2013.

4. Result and Discussion

The results of analysis have been conducted to address main and specific objectives

of the research. This part is divided in to five major sections. The first section of this

part presents socio-economic characteristics of respondents. Loan and related

characteristics of respondents in the study area are presented in the second

section. In the third section, business and related characteristics of respondents are

analyzed. Challenges and problems of institution are presented in forth section. In

last section presents the econometric analysis that identifies the most important

determinants that affect borrowers’ loan repayment performances.

In generally, this part presents the results from the descriptive and econometric

analysis. The descriptive analysis made use of tools such as mean, standard

deviation, percentage, and frequency distribution. In addition the t-test and chi-

square statistics were employed to compare defaulters and non defaulters group

with respect to some explanatory variables.

4.1. Descriptive Analysis

The socio-economic, loan and business related characteristics of the respondents

such as sex, age, educational level, marital status, average family size, average

number of dependents, saving behavior, disbursement, repayment period,


EJBE 2014, 7 (13) Page | 35

repayment status, loan sufficiency, loan diversion, business types, business related

experience, repeatedly borrowing, training, supervisions, and the related variables

of defaulters and non defaulters were analyzed by using descriptive statistics.

4.1.1. Socio-Economic Characteristics of Respondents Based on

Continuous Variables

In Table 2 disclosed that, age of sample respondents range from 24 to 58 years.

Accordingly, the respondents’ age range from 24 – 34 constitutes 43.6%, the age

range from 35 – 45 constitutes 45.9%, and the remaining 10.5% of respondents

were constituted under the age ranges of 46 – 58. The proportion of defaulters’ age

was highest in the age range of 24 – 34 by constituting 58.1%, and lower in the age

range of 46 – 58 representing 4.1%. While the non-defaulters respondents

comprises 54.1% in the age range of 35 – 45 and 16.9% in the range of 46 – 58.

Thus, this indicates that the borrowers at younger stages become more defaulter

than at older age. This is because as age of borrowers’ increases they became

settled and accumulate wealth; acquire experience in business management and

credit use than youngsters. Then these and related positive variables enables elder

borrowers to be better payers than youngsters.

Table 2: Age of Respondents

Variables Defaulters

(N = 148)

Non-defaulters

(N = 148)

Total sample

(N= 148)

X2-Value

Age

(in year)

24 -34 86 58.1% 43 29.1% 129 43.6% X

2=30.214

P = 000* 35 -45 56 37.8% 80 54.1% 136 45.9%

46 -58 6 4.1% 25 16.9% 31 10.5%

Source: Survey result, 2013.

From the following table 3, regarding to family size, sample respondents ranged

from 1-10 persons, with average family size of 4.23 and standard deviation of

2.001. Of these, the average family size of the defaulters was 4.51 and a standard

deviation of 2.23, while the average mean and standard deviation for non-

defaulters were 3.96, and1.706, respectively.

Table 3: Characteristics of Respondents Based on Family Size, and

Dependants

Variables

Defaulters

(148)

Non-defaulters

(N=148)

Total sample

(N=296)

t-test Mean St.dev Mean St.dev Mean St.dev

Family size (in number) 4.51 2.23 3.96 1.706 4.23 2.001 2.371*

Dependant(in number) 4.01 1.909 3.39 1.482 3.70 1.737 3.162*

Source: Survey result, 2013. * Significant at 5% probability level

Therefore, based on the survey result the average family size of defaulters is

greater than the average family size of non-defaulters. This indicates that as family

size in the household of borrowers’ increases then they allocate their business


Page | 36 EJBE 2014, 7 (13)

incomes, which was financed by credit loan, to cover different household’s

expenses. As a result, this impacts the borrowers’ loan repayment performance

negatively. The significance value is .018, which is less than .05; therefore based on

this researcher can say that there is a significant difference between defaulters at

5% significance level (table 3).

4.1.2. Socio-Economic Characteristics of Respondents Based on Discrete

Variables

As regard to marital status, from the total sample respondents 72.6%, 17.2%, 6.4%,

and 3.7% were married, single, divorced and widowed, respectively. The marital

statuses of defaulters were married, single, divorced, and widowed with the

percentage of 68, 21.1, 6.8, and 4.1, respectively. Whereas the marital statuses of

non-defaulters were married, single, divorced, and widowed with the percentage

of 77.2, 13.4, 6, and 3.4, at the same order. Statistically, it was found that the

percentage differences between the two groups were insignificant (Table 4). This

indicates that being single, married, divorced, and widowed have the same status

either to repay or not to repay.

As regards to sex composition, 94(31.8%) were female respondents, whereas,

202(68.2%) were male respondents. The proportion of defaulters was 44(29.7%)

for females, whereas, 104(70.3%) for male counter parts. This reveals that from

their respective sex composition, females’ respondents were found having more

repayment performance than male respondents. However, the chi-square result

shows that the association between sex and loan repayment is insignificant (X2=

4.561, at P=.454) table 4. This indicates that being either sex doesn’t determine

loan repayment rate.

Table 4: Marital Status, Sex, and Income Source of Respondents

Marital

status

Defaulters

(N=148)

Non-defaulters

(N=148)

Total sample

(N=296)

X2-Value

N P N P N P

Single 31 21.1% 20 13.4% 51 17.2%

X2

= 4.549

P= .314**

Married 100 68% 115 77.2% 215 72.6%

Divorced 10 6.8% 9 6% 19 6.4%

Widowed 6 4.1% 5 3.4% 11 3.7%

Sex Male 104 70.3% 98 66.2% 202 68.2% X

2= .561

P= .454** Female 44 29.7% 50 33.8% 94 31.8%

Income

source

Agriculture 64 43.2% 82 55.4% 146 49.3% X2=7.967

P=.019*

Business 62 41.9% 39 26.4% 101 34.1%

Salary 22 14.9% 27 18.2% 49 16.6%

Source: Survey results, 2013 * significant association ** Not significantly associated

N= number of respondents, P = number of respondents’ percentage.

According to income source of respondents, the survey result shows that 49.3% of

respondents had their income from agriculture, 34.1% had from business which

was generated by loan, and 16.6% had from salary. From these respective source of


EJBE 2014, 7 (13) Page | 37

income, the proportion of defaulters were found more from respondents who had

their income from the business only, which was financed from the loan 62(41),

than the respondents who had additional source of incomes from agriculture

64(43.2), and salary 22(14.9%) table 4. In same line research, Acquah and Addo

(2011) reveal that lack of alternative income generating activity have the

probability of high loan default.

Table 5 disclosed that, from the total respondents, 21.6% respondents are

illiterates, 22.6% respondents are primary school (1-8), 19.3% respondents are high

school (9-19), 17.9% respondents are certificates, and 18.6% respondents are

diploma and above.

The educational level of defaulters were: 34.5% respondents illiterates, 33.8%

respondents primary school (Grade1-8), 13.5% respondents high school(Grade 9-

12), 10.1% respondents are certificates, and 8.1% respondents are diploma and

above, while for the non defaulters it is 8.8%, 11.5%, 25%, 25.7%, and 29.1 at the

same order. This indicates that the level of education and dependant variable has

direct relationship. It has positive implication on loan usage and managing the

business or using loan for income generating activities. Statistically, the chi-square

results also confirmed the presence of strong and significant association between

educational level and loan repayment rate at 0.05 significance level (X2= 69.017, P =

000).

Table 5: Respondents Characteristics Based on Educational Attainment

Variable

Defaulters

(N=148)

Non-defaulters

(N =148)

Total sample

(N =296)

X2-

Value N P N P N P

Education

Level

Illiterates 51 34.5% 13 8.8% 64 21.6%

X2=

69.017

P =

000*

1-8 50 33.8% 17 11.5% 67 22.6%

9-12 20 13.5% 37 25% 57 19.3%

Certificate 15 10.1% 38 25.7% 53 17.9%

≥Diploma 12 8.1% 43 29.1% 55 18.6%

Source: Questionnaire, 2013. * Significantly associated

4.1.3. Loan and Related Characteristics of Respondents Based on Discrete

Variables

With respect to lending methodology about 64.2% of respondents were engaged in

group lending scheme, while 35.8% of respondents were borrowed loan under

individual lending scheme. The finding indicates that 61.5% of defaulter’s

proportions were involved in group lending methodology, whereas the remaining

38.5% of defaulters were categorized under individual lending methodology. In

group lending methodology respondents had the chance to gate loan easily

without formal collateral and personal guarantee, joint liability of group members

used as collateral.


Page | 38 EJBE 2014, 7 (13)

According to group member relationships of respondents, they formed group with

family 14.8%, friends32.8%, and neighbors52.8%. Of these, the proportion of

defaulter borrowers were group formed with family13.2%, friends 30.8%,

neighbors56%. More than half of defaulter respondents were found in group

formed with neighbors.

With regarding to purpose of loan was borrowed, 105(35.5%) borrowers were used

loan for the purpose of starting new businesses, whereas, 191(64.5%) of

respondents used loan to expand the already existing businesses. The proportion of

defaulters’ respondents were a little bit more in borrowers who are engaged in

already existing businesses (51.4%) than those who were in new businesses

(48.6%).

With the relation to loan utilization, 78% of respondents were utilized it for

intended purposes of businesses, while 22% of respondents have diverted the loan

for unintended purposes. 91.9% of respondents who utilized the loan to intended

purposes were found as non defaulters, whereas, 8.1% of non-defaulters

proportion were from those who are diverted the loan. Finding also shows that out

of 65(22%) borrowers, who have diverted the loan, 53(35.8%) of respondents were

found as defaulters, while from 231(78%) borrowers, who were utilized the loan for

intended purposes, only 95(64.2%) were defaulters. As per survey respondents the

main reason to divert loan were to repay another credit14 (21.5%, consumption

purposes16 (24.6%), covering educational fees for their children15 (23.1%), and to

engage in other businesses 20 (308%). However, out of those borrowers who have

diverted the loan to run another business (91.7%) were found as non-defaulters

while those who are diverted loan for other purposes become defaulters.

Statistically, chi-square result also confirms that there is strong and significance

association between loan diversion and loan repayment at 5% (X2= 33.139, at P =

000) could find from Table 6.

In terms of sufficiency of loan size, loan amount was found sufficient by the 64.2%

of the respondents, while the remaining 35.8% complained that it was not

sufficient for their intended purpose. Out of defaulter respondents more numbers

of defaulters (54.1%) was those respondents who complained loan as it was not

sufficient, whereas, 82.4% of respondents of non defaulters group were those

borrowers who found loan size as sufficient to their purposes. Therefore, there is a

mismatch of loan size and borrowers’ intended purpose has some implication for

loan diversion could find from Table 6. Statistically, chi-square result reveals the

strong and significant association between loan size and loan repayment at

significant level 5% (X2=42.857, at P = 000).

In regarding to training, majority of respondents 83.1% indicated that they had

received some kind of training on business and about institutional services before

receiving loans, while 16.9% responded that they had not received any training

before receiving loans. However 49% of respondents commented it as useful but

not organized, whereas, remaining 41% of them found it very useful and organized.


EJBE 2014, 7 (13) Page | 39

The proportions of defaulter respondents were higher in respondents group who

judged training was useful but not organized, 79(72.5%). On the other hand, only

30(27.5%) of respondents who replied training was very useful and organized

become defaulters.

Table 6: Characteristics of Lending Scheme, Purpose, Usage, and Size of

Loan

Variables

Defaulters

Non-

defaulters

Total sample

(N=296)

X2-

P value N P

Loan scheme

Group lending 91(61.5) 99(66.9%) 190 64.2% X

2=.941

P=.332 Individual

lending 57(38.5%) 49(33.1%) 106 35.8%

Reason for

involving in

group

Easy to gate

loan 36(39.6%) 47(48%) 83 43.9%

X2= 5.152

P= .076 It is mandatory 43(47.3%) 31(31.6%) 74 39.2%

Friends’

initiation 12(13.2%) 20(20.4%) 32 16.9%

Group

members

relationship

Family/relatives 12(13.2%) 16(16.3%) 28 14.8% X

2=.985

P=.611 Friends 28(30.8%) 34(34.7%) 62 32.8%

Neighbors 51(56%) 48(49%) 99 52.4%

Purpose of loan

Starting new

business 72(48.6%) 33(22.3%) 105 35.5%

X2=

22.449

P= 000*

Expanding

existing

business

76(51.4%) 115(77.7%) 191 64.5%

Loan usage

For intended

business 95(64.2%) 136(91.9%) 231 78% X

2=

33.139

P = 000* For unintended

purpose 53(35.8%) 12(8.1%) 65 22%

Is Loan size

sufficient

Yes 68(45.9%) 122(82.4%) 190 64.2% X2=42.857

P = 000* No 80(54.1%) 26(17.6%) 106 35.8%

Reason of

spending for

unintended

purpose

To repay

another credit 14(26.4%) 0% 14 21.5%

X2=25.917

P =000*

To engage in

other business 9(17%) 11(91.7%) 20 30.8%

To cover

educational fee 14(26.4%) 1(8.3%) 15 23.1%

Consumption

purpose 16(30.2%) 0 16 24.6%

Source: Survey result, 2013. N = number of respondents P = percentage of respondents

As table 7 shows almost all respondents of non-defaulters were those who took

training on business. Hence, the training variable has direct impact on loan

repayment performance either to increase or decrease defaulting rate. Statistically,

chi-square also confirms the presence of strong and a significant association


Page | 40 EJBE 2014, 7 (13)

between training and dependant variable at 5% level of significance (X2=41.741, at

P=000)

Table 7: Training, Disbursement, and Repayment Period

Variables

Defaulters

(N=148)

Non

defaulters

(N= 148)

Total sample

(N=296)

X2-value

N P N P No. P

Availability of

training

Yes 109 73.6% 147 99.3% 246 83.1% X2=41.741

P = 000 No 39 26.4% 1 0.7% 50 16.9%

How was

training

Very useful and

well organized 30 27.5% 75 51% 105 41%

X2= 14.284

P = .000* Useful but not

organized 79 72.5% 72 49% 151 49%

Disbursement

Time

Timely release 73 49.3% 116 78.4% 189 63.9% X2=27.063

P=000* Delay 75 50.7% 32 21.7% 107 36.1%

Reason for

delay

Unavailability

of employees 17 23% 12 37.5 29 27.4%

X2 = 2.376

P =.305

Unnecessary

appointment 29 39.2% 10 31.2% 39 36.9%

Long

procedure 28 37.8% 10 31.2% 38 35.8%

Repayment

period

Suitable 70 47.3% 136 91.9 206 69.6 X

2 =69.546

P=000* Unsuitable 78 52.7% 12 8.1% 90 30.4

Reason for

unsuitability

Grace period is

too short 24 30.8% 3 27.3% 27 30.3

X2= .056

P =.813 Repayment

period is short 54 69.2% 8 72.7% 62 69.7

Saving type Voluntary 27 18.2% 64 43.2% 91 30.7% X

2= 21.722

P = 000*

Saving place SMFI 130 87.8% 122 82.4% 252 85.1% X

2=1.709

P =.191 Banks 18 12.2% 26 17.6% 44 14.9%

Residence

place near to

SMFI

Yes 78 52.7% 117 79.1% 195 65.9% X

2=22.859

P=000* No 70 47.3% 31 20.9% 101 34.1%

Business place

near to SMFI

Yes 77 52% 123 83.1% 200 67.6% X2= 32.622

P=000* No 71 48% 25 16.9% 119 32.4%

Source: Survey result, 2013. * Significant association

With respect to loan disbursement, 63.9% of respondents have been answered that

they took loan timely. On the other hand, 36.1 % of respondents portrays that loan

disbursement was delayed for number of weeks. The table 7 below shows that the

higher proportion of non-defaulters 78.4% were found from those respondents

who received timely disbursed loan, while only 21.7% of non-defaulter respondents

were a group from who have received the loan delayed on disbursement.


EJBE 2014, 7 (13) Page | 41

According to respondents, this delay of disbursement was due to the absence of

loan officers and managers on the work time 27.4%, unnecessary appointment

36.9%, and taking long procedure to finish precondition to deliver loan service

35.8%. The chi-square result also shows the presence of strong and significant

association between disbursement and dependant variable at 5% significance level

(X2=27.063, at P=000).

In regard to suitability of repayment period, 69.6% of respondents indicated that

the loan repayment period was suitable; on the other hand 30.4% of other

respondents revealed it was not. Based on findings, more defaulters’ number (78%)

of respondents was found in the group that replied period was not suitable, while

large numbers of non-defaulters (91.9%) were those who reported period was

suitable. For the 30.4% respondents who disagreed on suitability of period; the

main reason was the shortness of grace and repayment period. The chi-square

result also shows the presence of strong and significant association between

repayment period and dependant variable at 5% significant level(X2

= 69.546, at

P=000) (table 7).

As per saving services, all the SMFI’s service users are required to save compulsory

saving as mandatory since it seen as collateral. However out of total respondents

91(30.7%) said that they have voluntary saving account in plus to compulsory

saving, and on other hand from total respondents 44(14.9%) of them reported that

they save their money in the other formal banks. This indicates the voluntary saving

among the sample clients was not sufficient. This may be because of the low

awareness of clients. Out of voluntary savers respondents 64(70.3%) of them are

non-defaulters. Therefore, the voluntary saving has positive implication on

repayment rate disclosed by table7.

As table 7 shows most of respondents’ residence and business place were near to

lending institution office in 65.9% and 67.6% respectively. These enable loan

officers to make continuous supervision as well as advisory visits on the way of

borrowers’ loan usage and loan repayment.

4.1.4. Loan characteristics based on continues variables

The average business experience of the respondents was 2.04 years with the

standard deviation of 1.414. The mean business experience of defaulters was 1.80

with the standard deviation of 1.375, while that of non-defaulters was 2.28 with

the standard deviation of 1.414. From the t-test, 2-tailed is less than 0.05. This

means that there was a significant difference between defaulters and non-

defaulters groups in terms of business experience at 5% significance level (Table 8).

This implies that having the more business experiences enhances the probability to

repay loan more than having less business experience.

On an average, respondents obtained the loan credits for 2.84 rounds with the

standard deviation of 1.405. It was found that defaulters had credit 1.95 rounds


Page | 42 EJBE 2014, 7 (13)

while non-defaulters had 3.62 rounds with a standard deviation of 0.994 and 1.241

respectively. Moreover, the mean difference between defaulters and non-

defaulters was statistically significant at 5 % level (table 8). This implies that if client

borrow loan for the number of rounds, then they aware obligation and

responsibility on loan usage as well as repayment more than those who are the

first time borrowers.

With respect to supervision and advisory visits of borrowers, on average the

sample respondents have got 3.19 times supervision and visit services. The mean

supervision of defaulters was 2.24 times with the standard deviation of 1.351 and

that of defaulters was supervised 3.67 times with the standard deviation of 1.002.

The significance value is 000 (which is less than 0.05). Therefore there is a

significant statistical difference between defaulters and non-defaulters in these

averages, at 5% significance level (Table 8). This indicates that the continuous

follow up of borrowers reminds them to pay attention toward their business and

enables to increase their perception of responsibility toward loan repayment.

Table 8: Summary of continuous variables

Variables

Defaulters

(148)

Non-defaulters

(N=1480)

Total sample

(N=296)

t-test Mean St.dev Mean St.dev Mean St.dev

Business experience 1.80 1.375 2.28 1.414 2.04 1.414 -3.000*

Repeatedly borrowing 1.95 .994 3.62 1.241 2.84 1.405 -11.644*

Supervision/visits 2.24 1.351 3.67 1.002 3.19 1.313 -7.367*

Source: Survey result, 2013. * Significance at 5% level.

4.1.5. Business Related Characteristics

Respondents were found to engage in various business activities. For this study

purpose the most important businesses are categorized in to two parts namely

agricultural type businesses(cattle fattening and selling, and selling of coffee, chat,

grain, and other staple crops) and non-agricultural type businesses (shop and kiosk,

handcrafts, small restaurant and cafeteria, and service providing like beauty salon,

barbering).

With respect to the purpose for which loan was taken, 40.5% of sample

respondents took the loan to engage in agricultural type business in rural areas,

whereas, another activity for which most of the borrowers took loan was non-

agricultural type businesses, 59.5%.

In table 9, the survey result shows that the proportions of defaulters were 56.1%

and 43.9% for agricultural and non-agricultural businesses respectively.

On another hand, 75% of the respondents, those who engaged in non agricultural

type businesses, have repaid their loan better than those who were engaged in

agricultural type businesses, 25%. In generally the survey result indicates that

respondents who engaged in non-agricultural types of business was found that


EJBE 2014, 7 (13) Page | 43

they had better loan repayment performance than respondents who were engaged

in the agricultural types of businesses. This indicates that the type of business may

have some implications on the loan repayment performance.

Table 9: Business types of defaulters and non defaulters

Business types

Defaulters

(N=148)

Non defaulters

(N=148)

Total sample

(N=296)

N P N P N P

Cattle fattening and selling 54 36.5% 21 14.2% 75 25.3%

Selling of coffee, chat, grain and

other staple crops 29 19.6% 16 10.8% 45 15.2%

Shop and kiosk 18 12.2% 38 25.7% 56 18.9%

Handcrafts 23 15.5% 26 17.6% 49 16.6%

Small restaurant and cafeteria 12 8.1% 32 21.6% 44 14.9%

Service providing 12 8.1% 15 10.1% 27 9.1%

Total 148 100% 148 100% 296 100%

Source: survey result, 2013

N = number of respondents P = percentage of respondents

4.1.6. Business Specific Characteristics (Discrete Variables)

In terms of business research, almost all respondents did not conduct formal

marketing research before their engagement in certain businesses. As the survey

result shows that the reasons for not conducting market research were: some of

them had business experience from their pre-existing businesses before already

starting new business53.4%, had knowledge from their friends’ businesses 34.5%,

and uses different mass media sources like television and radio 12.2% (table 10).

Table 10, which was constructed from survey data, reveals that almost 43.9% of

defaulters’ business was successful while 81.1% of non-defaulters business was

successful. However due to many reasons defaulters are not willing to repay their

loan. According to defaulters, their plan to pay their loan was borrowing other

sources of credit 4.6%, selling their assets 45.5%, from their income sources 17.4%,

and 32.1% of respondents replied that they have no any sources to pay the loan.

Table 10: Business Information and Its Successfulness

Variables

Defaulters

(N = 148)

Non

defaulters

(N = 148)

Total sample

(N = 296)

X2-Value

N P N P N P

Business

information

Own experience 56 37.8% 102 68.9% 158 53.4% X

2=4.131

P = 000* Friends’ business 81 54.7% 21 14.2% 102 34.5%

Mass media 11 7.4% 25 16.9% 36 12.2%

Business

successful

Yes 65 43.9% 120 81.1% 185 62.5% X2=43.604

P = 000* No 83 56.1% 28 18.9% 111 37.5%

Source: survey result, 2013. *Significance at 5% level. N=number of respondents, P=percentage of

respondents.


Page | 44 EJBE 2014, 7 (13)

According to loan repayment status of respondents, it was found that 34.8% of

non-defaulters had fully repaid on maturity time, and only 4.5% of them paid it

fully but too late. The fully repayment of loan enables non-defaulters to gate the

advantages of next higher loan 13%, having good relationship with the lending

institution 13%, keeping their socially status in the society 7.2%, realizing their

freedom from any penalty 6.1%.

Whereas, according to defaulters group, 30.3% of respondents had paid loan

partially on maturity period and 9% of the same group had paid too late.

As elaborated above topics, the SMFI have been giving micro loan to rural and

urban poor to engage in various business activities through different terms of loan.

However, some borrowers (defaulters) have been challenging the institution’s loan

collection performance.

4.2. Challenges and Problems of Institution

4.2.1. Internal Challenges

• Lack of adequate loan or equity capital to increase loan able fund.

• Management information problem/ system constraint.

• The presence of highly experienced employees’ turn over to other

governmental and non-governmental organizations.

4.2.2. External Challenges

Omo Micro Financing Institution(OMFI) is one that is highly competing in the same

area of institution. In addition, the other source of competition rises from local

cooperatives and some traditional associations like Ikub, in relation to saving

mobilization.

4.3. Determinants of Borrowers’ Loan Repayment Performance in SMFI

The result of binary logistic model on determinants of loan repayment performance

of borrowers is presented in table 11. A total of 14 explanatory variables were

considered in the econometric model. Out of which nine variables were found to

be significant. These were age, education, time laps between loan application and

disbursement, loan size, loan diversion, repayment period, number of dependants,

training, and supervision. The coefficients of these all significant variables were

negative except education level and time laps between loan application and

disbursement.

On the other hand, five variables were found insignificant on dependent variable

namely family size of respondents, repeatedly borrowing, business experience,

agricultural type business, and non-agricultural type business. Overall, the binary

logistic model successfully predicted factors contributing to 89.9% of micro credit

loan repayment problem among SMFI.


EJBE 2014, 7 (13) Page | 45

1. Age of respondents: the age variable was negatively and significantly

influencing loan repayment at 1% significant level. If the other variables held

constant, a unit increase in the respondents’ age decreases the probability of

being defaulter by.055 (table 11). This implies that through time aged

respondents acquire experience in business, became settled and accumulate

more wealth than youngsters.

2. Education level: the education level was positively and significantly influencing

loan repayment at 1% significance level. An increase in one year schooling

increases the probability of the loan repayment rate by 4.939, ceteris paribus.

This figure revels that the borrowers whose educational level increased have

the probability of increasing the loan repayment performance four times more

than the borrowers who have lesser education level/ illiterates (table 11). This

suggests that more educated borrower may have access to business

information.

3. Time laps between loan application and disbursement: This variable was

positively and significantly influencing borrowers’ loan repayment performance.

It became significant predictor of borrowers’ loan repayment performance at

5% significance level. As indicated table 11, timely disbursement of loan

increases the borrowers’ loan repayment probability by 3.369 (table 11).

Therefore, these positive preconditions enable borrowers to enhance loan

repayment performance better.

4. Loan size: this variable also was found to influence borrowers’ loan repayment

performance negatively and significantly at 1% significance level. Keeping the

other factors constant, having sufficient loan size and operating business with

adequate amount of capital decreases the probability of being defaulter by

0.165 (table 11).

5. Loan diversion: this variable was found to influence negatively and significantly

the borrowers’ loan repayment performance at 1% significance level. An

application of entire loan for intended and productive business lessens the

probability of defaulting by 0.084 (table 11).

6. Repayment period: this variable was found to influence negatively and

significantly the borrowers’ loan repayment rate at 1% significance level. The

arrangements of suitable loan repayment period for borrowers decrease the

chance of being defaulter by 11.4% (table 11).

7. Number of dependants within and out household: This variable was found to

determine negatively and significantly borrowers’ loan repayment performance

at 1% significance level. If other variables held constant, having non-dependants

or lower number of dependants’ decreases the probability of defaulting by the

15.8%.


Page | 46 EJBE 2014, 7 (13)

8. Training: this variable was also found to influence negatively and significantly

the borrowers’ loan repayment performance at 1% significance level. If other

variables holds constant, the delivering of well organized and sufficient training

properly for borrowers lessen the probability of being defaulter by 0.016.

9. Supervision and advisory visits: this variable was to have negative and

significant association with the dependant variable. It is significant predictor

loan repayment performance at 1% significance level. If other variables held

constant, continuous follow up and visit of respondents reduces their

probability of being defaulter by 0.102.

Table 11: Results of Binary Logistic Model

Independent variables B SE Sig. Exp(B)

1. Age of the respondents -2.892 .685 000 .055

2. Education level of the respondents 1.597 .572 .005 4.939

3. Family size in the household -.791 .611 .195 .453

4.Time laps between loan application and

disbursement 1.215 .497 .015 3.369

5. Repeatedly borrowing -.690 .698 .323 .502

6 Loan size -1.799 .501 000 .165

7. Loan diversion -2.474 .638 000 .084

8. Business types(Agri. Type and Non-Agri. type) -.474 .601 .430 .623

9. Business experience .826 .576 .151 2.285

10. Repayment period -2.172 .521 000 .114

11. Number of dependants -1.846 .665 .005 .158

12. Training -4.161 1.182 000 .016

13.Supervision and advisory visits

-2.286 .609 000

.102

Source: Survey result, 2013. B=regression coefficient, Exp (B) = odds ratio

Overall, correct prediction = 89.9% Sig. = significance S.E = standard error

-2 Loglikelihood = 140.640 Cox & Snell R Square = .598

5. Summary, Conclusion and Recommendation

5.1. Summary

Micro financing institution services is very useful to enhance people’s livelihood

providing credit for those poor. For micro financing institution achieving highest

loan repayment performance enables to accomplish this mission for expanding and

delivering quality services to the poor without suffering financial shortages. The

poor loan repayment performance undermines the financial position of MFI, which

further hiders the cyclical flow of funds between institution and borrowers. The

financial data of SMFI indicates that the defaulting rate of institution has been

shown the decreasing rate since 2009 to 2012. However in the year of 2013 the

defaulting rate becomes again increased.


EJBE 2014, 7 (13) Page | 47

Therefore, this study was intended to identify and analyze determinant factors

which affect borrowers’ loan repayment performance in SMFI. For data analysis

purpose both descriptive statistics and binary logistic model were employed.

The descriptive statistics findings shows that there were significant association

between dependant variable with respect to education level, loan diversion, loan

size, training, time laps between loan application and disbursement, repayment

period and all business types. Researcher found the significance difference

between defaulters with respect to age, family size, and number of dependants,

business experience, repeatedly borrowing, and supervision. On the other hand,

fourteen explanatory variables were entered in to binary logistic model and out of

which nine variables were found significant to determine loan repayment

performance of borrowers except family size, business experience, repeatedly

borrowing, agricultural types businesses, and non-agricultural types businesses.

The descriptive statistics result shows that the percentages of defaulter

respondents decrease from youngsters group to elders group. Proportion of

defaulting group was higher in respondents who were illiterate and obtained

elementary school level, whereas, the percentages of non-defaulters increases as

their level of education increases.

Similarly for about 69.6% of respondents’ loan repayment period was found

suitable. Further, almost 83.1% of respondents have got training services. However,

the majority of respondents commented the quality of training was useful but not

organized.

The sample respondents were engaged in different types of businesses both in

urban and rural areas. The highest proportion of defaulters respondents were

found in the agri-based business. Whereas, better repayment performance had

shown, who engaged in the businesses of provision shop, handcrafts, small

restaurant and service providing.

About 43.9% of defaulters’ businesses were successful and they could able to pay.

However, due to other reasons they were not willing to pay. The remaining

defaulters were unable to pay due to business failure.

5.2. Conclusion and Recommendation

The finding of this study revealed that the age of respondents negatively and

significantly determines the loan repayment performance of borrowers. This

indicates that the elder respondents have better repayment performance than

youngsters. And the elders were more responsible to repay loan than youngsters.

The researcher not recommends excluding youngsters. However, the care must be

taken when starting from applicants’ screening to through repayment periods, the

special attention for follow up and supervision is necessary.


Page | 48 EJBE 2014, 7 (13)

The education level determines loan repayment positively and significantly. The

borrowers who attained higher education level able to pay better than the

borrowers who were in lower level schooling and/or illiterates. Therefore,

institution should motivate educated people and also easy to provide training.

Time lag between loan application and disbursement should be reduced to increase

repayment rate. The complicated loan processing procedures, which might lead to

delay in disbursement, further, it will increase default rate.

The supervision made by the loan officers and borrowers ratio should be reduced

and it leads to increase follow-up services. However, it is recommended that

institution should compute thoroughly the borrowers’ business proposal loan size

before approving and sanctioning.

Borrowers who have small number of or no dependants in the household perform

better in loan repayment. The borrowers who support large number of dependants

also perform well with proper supervision.

Loan diversion was also found as essential and significant determinant of loan

repayment rate negatively. This means, diverting loan into non-income generating

activities increases default rate. Therefore, it is recommended that the institution

should give attention to continuous follow-up on proper loan utilization.

Repayment period is also found to be a significant determinant of loan repayment

performance of borrowers. Suitability of loan repayment period for borrowers was

found to significantly increase the probability of repaying loan. Therefore, the

institution has to give enough time to clients so that they will be able to work with

the loans they have borrowed and arrange the time to collect loan that will be

suitable for them to sell their business output.

Finally, this study has focused on certain variables related to determinants of loan

repayment performance of borrowers. However, loan repayment performance on

behalf of institution was not investigated. Thus, further researches can conduct on

this issue to breach the gap in this area.

References

Abreham (2002) “Loan repayment and its Determinants in Small-Scale Enterprises Financing

in Ethiopia: Case of private borrowers Around Zeway Area”, AAU.

Acquah H.D. and Addo J. (2011), “Determinants of loan repayment performance of

fishermen: Empirical evidence from Ghana.” Department of agricultural economics and

extension, University of Cape Coast, Ghana Vol. XLIV , No. 4 (148).

Addisu M.(2006) “Micro-finance Repayment Problems in the Informal Sector in Addis

Ababa.” Ethiopian Journal of Business & Development Volume 1 Number 2.

Bayeh A.K. (2012) “Financial Sustainability of Microfinance Institutions (MFIs) in Ethiopia.”

European Journal of Business and Management Vol 4, No.15.


EJBE 2014, 7 (13) Page | 49

Besley T. and. Coate, S. (1995) “Group Lending, Repayment Incentives, and Social Collateral.”

Journal of Development Economics 46, no. 1: 1-18.

Eze C.C. and Ibekwe U.C. (2007) “Determinants of loan repayment under the indigenous

financial systems in Southeast.” Nigeria’ Journal of social science 2(2) 116 – 120, 2007.

Fikirte K.R (2011) “Determinants of loan repayment performance: A case study in the Addis

Credit and Saving Institution.” Addis Ababa, Ethiopia

Idoge D.E.(2013) “Regionalizing Loan Repayment Capacity of Small Holder Cooperative

Farmers in Nigeria: Exploring South-South Nigeria.” Journal of Biology, Agriculture and

Health care Vol.3, No.7

Nawai, N. and Shariff, M. N. M. (2013) “Loan Repayment Problems in Microfinance Programs

that use Individual Lending Approach: A Qualitative Analysis.” Journal of Transformative

Entrepreneurship Vol. 1, Issue 2, pp.93-99.

Oke, J.T.O., Adeyemo, R. and Agbonlahor, M.U. (2007) “An Empirical Analysis of Microcredit

Repayment in Southwestern Nigeria.” Humanity & Social Sciences journal 2 (1):63-74, ISSN

1818-4960.

Sileshi M., Nyikal R. and Wangia S. (2012) “Factors Affecting Loan Repayment Performance

of Smallholder Farmers in East Hararghe, Ethiopia.” Developing Country Studies, Vol 2,

No.11.

Tundui C. and Tundui H. (2013) “Microcredit, Micro Enterprising and Repayment Myth: The

Case of Micro and Small Women Business Entrepreneurs in Tanzania.” American Journal of

Business and Management Vol. 2, No. 1 pp. 20-30.

Wongnaa C. A. and Awunyo-Vitor D. (2013) “Factors affecting loan repayment performance

among Yam farmers in the Sene district, Ghana.” Agris on-line Papers in Economics and

Informatics Vol. No. 2.

Documents

Performance of Loan Repayment Determinants in Ethiopian Micro … · 2014-07-07 · reference of Addis Credit and Saving Institution, Addis Ababa, Ethiopia (Fikirte, 2011). She applied,